Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative sear...

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Vydané v:	IET control theory & applications Ročník 13; číslo 18; s. 3040 - 3051
Hlavní autori:	Ma, Hao, Pan, Jian, Ding, Feng, Xu, Ling, Ding, Wenfang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 17.12.2019
Predmet:	autoregressive moving average noise autoregressive moving average processes gradient methods identification problem information vector iterative methods iterative parameter estimation iterative search least squares approximations multivariable output‐error‐like autoregressive moving average systems multivariable output‐error‐like system parameter estimation parameter vector recursive least squares algorithm redundant parameter estimates Research Article unknown variables vectors iterative methods least squares approximations autoregressive moving average processes multivariable output-error-like autoregressive moving average systems recursive least squares algorithm redundant parameter estimates vectors autoregressive moving average noise parameter vector iterative search information vector identification problem parameter estimation multivariable output-error-like system iterative parameter estimation unknown variables gradient methods
ISSN:	1751-8644, 1751-8652
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Abstract	This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.
AbstractList	This study considers the parameter estimation of a multi‐variable output‐error‐like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares‐based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.
Author	Pan, Jian Ding, Wenfang Ding, Feng Xu, Ling Ma, Hao
Author_xml	– sequence: 1 givenname: Hao orcidid: 0000-0001-9212-3363 surname: Ma fullname: Ma, Hao organization: 1Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, People's Republic of China – sequence: 2 givenname: Jian surname: Pan fullname: Pan, Jian organization: 1Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, People's Republic of China – sequence: 3 givenname: Feng orcidid: 0000-0002-2721-2025 surname: Ding fullname: Ding, Feng email: fding@jiangnan.edu.cn organization: 3School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 4 givenname: Ling surname: Xu fullname: Xu, Ling organization: 3School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, People's Republic of China – sequence: 5 givenname: Wenfang surname: Ding fullname: Ding, Wenfang organization: 1Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, People's Republic of China
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Keywords	iterative methods least squares approximations autoregressive moving average processes multivariable output-error-like autoregressive moving average systems recursive least squares algorithm redundant parameter estimates vectors autoregressive moving average noise parameter vector iterative search information vector identification problem parameter estimation multivariable output-error-like system iterative parameter estimation unknown variables gradient methods
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Snippet	This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the... This study considers the parameter estimation of a multi‐variable output‐error‐like system with autoregressive moving average noise. In order to solve the...
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SubjectTerms	autoregressive moving average noise autoregressive moving average processes gradient methods identification problem information vector iterative methods iterative parameter estimation iterative search least squares approximations multivariable output‐error‐like autoregressive moving average systems multivariable output‐error‐like system parameter estimation parameter vector recursive least squares algorithm redundant parameter estimates Research Article unknown variables vectors
Title	Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems
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